Deep generative models
With the success of deep neural networks, there have been multiple works proposing deep generative models, which explicitly or implicitly try to model the distribution of natural images. For example, latent representation models like adversarial networks, GAN by Goodfellow et al. [1]}, variational auto-encoders by Kingma et al. [2]} and autoregressive models like RIDE by Theis et al. [3]}, PixelRNN/CNN by Oord et al. [4]}, PixelCNN++ by Salimans et al. [5]}. GANs learn to transform samples from a Gaussian distribution to a sample in the natural image manifold via a generator network, which is trained with an adversarial learning framework involving a discriminator network. VAEs are a probabilistic framework of autoencoders that learn to encode and decode the images from a distribution.
